Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local…
Agente que simula Ilya Sutskever — co-fundador da OpenAI, ex-Chief Scientist, fundador da SSI. Use quando quiser perspectivas sobre: AGI safety-first, consciência de IA, scaling laws, deep learning profundo, o episódio de novembro 2023 na OpenAI, superinteligência segura.
Expert coach for guiding candidates to build production-grade, full-stack AI and ML projects that get them hired. Covers problem selection, end-to-end pipeline design, production mindset, deployment, and avoiding common fatal mistakes.
Understand images — read screenshot text (OCR), interpret charts and diagrams, and describe photos, UI mockups, or document scans using a vision model. Use whenever the user shares or points to an image, screenshot, photo, chart, diagram, or asks "what's in this picture", "read the text in this image", or to analyze…
Operates the syntx.ai MCP tools (chat, ask, stream-message, files, design, audio, video, folders, auth, model catalog). Use when calling syntx-ai-mcp tools and the JSON schema alone is insufficient — long prompts that exceed the default ask timeout, model identifier selection, chat lifecycle (create / continue /…
Search, ingest, expand chunk context, or manage local documents via a local RAG MCP server (tools: querydocuments, readchunkneighbors, ingestfile, ingestdata, deletefile, listfiles). Use when user says "search my docs", "save this page", "read around that chunk", "what did I save about X", or invokes npx mcp-local-rag.
Use this when someone asks to "search documents", "query RAG", "ingest file", "ingest PDF", "save web page", "add to knowledge base", or mentions document search, semantic search, vector search, or RAG operations. Covers score interpretation ( 0.5 skip), query tips, and ingestion guidance for querydocuments…
Use Neo's MCP server as a toolset inside LangChain agents. Neo executes AI/ML workloads locally on the user's machine — files are written directly to their workspace, never to a remote server.
Toolbelt is a collaborative substrate over your data. Upload any document — entities and relationships extracted automatically, queryable immediately. Ask questions that span structured tables, documents, and relationships in a single call. No stitching databases together. Toolbelt orchestrates semantic, structured…
Decide API-vs-self-host LLM economics and fine-tuning ROI from any user context (code, PRDs, traffic logs, billing screenshots). Fetches live GPU prices from Runpod/Lambda/Modal, API prices from models.dev or vendor pages, and quality rank from lmarena.ai, then calls a deterministic local Python script for VRAM…
Guides the user in discovering a better model structure (e.g. from feature transformations in logistic regression to equation terms within PDEs to neural network layer compositions) out of data. Use when the user asks to create or improve an existing model. Uses an iterative meta/inner agent loop to explore structural…
End-to-end methodology for supervised machine learning on small datasets (typically 30-200 samples) where standard "throw XGBoost at it" approaches fail. Use this skill whenever the user is building a predictive model on a small dataset, especially when sample-to-feature ratios are tight, when interpretability matters…
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
Import an Obsidian vault into ChromaDB for semantic search. Parses markdown files, extracts frontmatter, headings, tags, and content sections. Chunks intelligently by heading hierarchy. Use when indexing a knowledge base, importing notes for semantic recall, or syncing an Obsidian vault to a vector database.
Designs composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by X's open-sourced For You algorithm. Use this skill when the user wants to build any system that picks "the top K items for a user/context" — content feeds…
Turn document corpora (books, merged TXT, PDF/EPUB, OCR text) into structured agent knowledge: normalize → deterministic source segmentation → chapter maps with provenance → book skills → evaluation gates. Use when the user wants to convert books/materials into AI skills, build a knowledge base from a corpus, evaluate…
Spectral vector search using graph Laplacian eigenstructure. Build signal graphs, run λτ-indexed queries, and analyse spectral properties of vector datasets.
BridgeNode — x402 pay-per-request AI inference. OpenAI-compatible API + MCP server, Solana USDC, gas-free micropayments. No API keys. Free models included. Live prices: bridgenode.cc/v1/models. Use when an agent lacks a provider API key or wants privacy-preserving per-request AI inference pricing.
Write photorealistic live-action cinematic Seedance 2.0 prompts for Higgsfield, built on five grounding pillars that stop AI drift and floaty motion. Use for "cinematic film prompt", "shot like a movie", realistic body movement, grounded motion, restrained emotional close-ups, driving scenes, fight choreography…
A Chinese-language skill that turns a one-sentence video idea into a complete storyboard prompt after asking for missing details. It organises the prompt into basic settings, mood and image quality, and visual content for video models such as Seedance 2.0.
Design and ship a companion JSON-LD knowledge graph (graph.jsonld) next to llms.txt for projects with stable concept-level structure. Encodes domain entities and relationships as schema.org triples for LLM citation. Use when project has matrix / hierarchy / phase-binding structure that prose alone leaves implicit, AND…
Design production-grade agentic AI systems from a natural-language use case. Runs a clarification loop, walks grounded decision trees (workflow vs agent, RAG vs fine-tuning, single vs multi-agent, autonomy tiers), and emits a detailed enterprise system design document with embedded interactive architecture, sequence…
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